Neural networks for EEG-MEG decoding with MNE-python and Tensorflow.
API reference is avalable in the Documentation.
Also check the example notebooks.
When using the implemented models please cite:
Zubarev I, Zetter R, Halme HL, Parkkonen L. Adaptive neural network classifier for decoding MEG signals. Neuroimage. 2019 May 4;197:425-434. link
@article{Zubarev2019AdaptiveSignals.,
title = {{Adaptive neural network classifier for decoding MEG signals.}},
year = {2019},
journal = {NeuroImage},
author = {Zubarev, Ivan and Zetter, Rasmus and Halme, Hanna-Leena and Parkkonen, Lauri},
month = {5},
pages = {425--434},
volume = {197},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811919303544 http://www.ncbi.nlm.nih.gov/pubmed/31059799},
doi = {10.1016/j.neuroimage.2019.04.068},
issn = {1095-9572},
pmid = {31059799},
keywords = {Brain–computer interface, Convolutional neural network, Magnetoencephalography}
}
@article{Lawhern2018,
author={Vernon J Lawhern and Amelia J Solon and Nicholas R Waytowich and Stephen M Gordon and Chou P Hung and Brent J Lance},
title={EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces},
journal={Journal of Neural Engineering},
volume={15},
number={5},
pages={056013},
url={http://stacks.iop.org/1741-2552/15/i=5/a=056013},
year={2018}
}